5,101 research outputs found
A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques
A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks
From Sensing to Predictions and Database Technique: A Review of TV White Space Information Acquisition in Cognitive Radio Networks
Strategies to acquire white space information is the single most significant
functionality in cognitive radio networks (CRNs) and as such, it has gone some evolution
to enhance information accuracy. The evolution trends are spectrum sensing, prediction
algorithm and recently, geo-location database technique. Previously, spectrum sensing was
the main technique for detecting the presence/absence of a primary user (PU) signal in a
given radio frequency (RF) spectrum. However, this expectation could not materialized as
a result of numerous technical challenges ranging from hardware imperfections to RF
signal impairments. To convey the evolutionary trends in the development of white space
information, we present a survey of the contemporary advancements in PU detection with
emphasis on the practical deployment of CRNs i.e. Television white space (TVWS) networks.
It is found that geo-location database is the most reliable technique to acquire
TVWS information although, it is financially driven. Finally, using financially driven
database model, this study compared the data-rate and spectral efficiency of FCC and
Ofcom TV channelization. It was discovered that Ofcom TV channelization outperforms
FCC TV channelization as a result of having higher spectrum bandwidth. We proposed the
adoption of an all-inclusive TVWS information acquisition model as the future research
direction for TVWS information acquisition techniques
WN: COGNET: Cognitive radio networks based on OFDM
Issued as final reportNational Science Foundation (U.S.
Contributions to the security of cognitive radio networks
The increasing emergence of wireless applications along with the static spectrum allocation followed by regulatory bodies has led to a high inefficiency in spectrum usage, and the lack of spectrum for new services. In this context, Cognitive Radio (CR) technology has been proposed as a possible solution to reuse the spectrum being underutilized by licensed services.
CRs are intelligent devices capable of sensing the medium and identifying those portions of the spectrum being unused. Based on their current perception of the environment and on that learned from past experiences, they can optimally tune themselves with regard to parameters such as frequency, coding and modulation, among others. Due to such properties, Cognitive Radio Networks (CRNs) can act as secondary users of the spectrum left unused by their legal owners or primary users, under the requirement of not interfering primary communications.
The successful deployment of these networks relies on the proper design of mechanisms in order to efficiently detect spectrum holes, adapt to changing environment conditions and manage the available spectrum. Furthermore, the need for addressing security issues is evidenced by two facts. First, as for any other type of wireless network, the air is used as communications medium and can easily be accessed by attackers. On the other hand, the particular attributes of CRNs offer new opportunities to malicious users, ranging from providing wrong information on the radio environment to disrupting the cognitive mechanisms, which could severely undermine the operation of these networks.
In this Ph.D thesis we have approached the challenge of securing Cognitive Radio Networks. Because CR technology is still evolving, to achieve this goal involves not only providing countermeasures for existing attacks but also to identify new potential threats and evaluate their impact on CRNs performance.
The main contributions of this thesis can be summarized as follows. First, a critical study on the State of the Art in this area is presented. A qualitative analysis of those threats to CRNs already identified in the literature is provided, and the efficacy of existing countermeasures is discussed. Based on this work, a set of guidelines are designed in order to design a detection system for the main threats to CRNs. Besides, a high level description of the components of this system is provided, being it the second contribution of this thesis.
The third contribution is the proposal of a new cross-layer attack to the Transmission Control Protocol (TCP) in CRNs. An analytical model of the impact of this attack on the throughput of TCP connections is derived, and a set of countermeasures in order to detect and mitigate the effect of such attack are proposed.
One of the main threats to CRNs is the Primary User Emulation (PUE) attack. This attack prevents CRNs from using available portions of the spectrum and can even lead to a Denial of Service (DoS). In the fourth contribution of this the method is proposed in order to deal with such attack. The method relies on a set of time measures provided by the members of the network and allows estimating the position of an emitter. This estimation is then used to determine the legitimacy of a given transmission and detect PUE attacks.
Cooperative methods are prone to be disrupted by malicious nodes reporting false data. This problem is addressed, in the context of cooperative location, in the fifth and last contribution of this thesis. A method based on Least Median Squares (LMS) fitting is proposed in order to detect forged measures and make the location process robust to them.
The efficiency and accuracy of the proposed methodologies are demonstrated by means of simulation
An Overview on Application of Machine Learning Techniques in Optical Networks
Today's telecommunication networks have become sources of enormous amounts of
widely heterogeneous data. This information can be retrieved from network
traffic traces, network alarms, signal quality indicators, users' behavioral
data, etc. Advanced mathematical tools are required to extract meaningful
information from these data and take decisions pertaining to the proper
functioning of the networks from the network-generated data. Among these
mathematical tools, Machine Learning (ML) is regarded as one of the most
promising methodological approaches to perform network-data analysis and enable
automated network self-configuration and fault management. The adoption of ML
techniques in the field of optical communication networks is motivated by the
unprecedented growth of network complexity faced by optical networks in the
last few years. Such complexity increase is due to the introduction of a huge
number of adjustable and interdependent system parameters (e.g., routing
configurations, modulation format, symbol rate, coding schemes, etc.) that are
enabled by the usage of coherent transmission/reception technologies, advanced
digital signal processing and compensation of nonlinear effects in optical
fiber propagation. In this paper we provide an overview of the application of
ML to optical communications and networking. We classify and survey relevant
literature dealing with the topic, and we also provide an introductory tutorial
on ML for researchers and practitioners interested in this field. Although a
good number of research papers have recently appeared, the application of ML to
optical networks is still in its infancy: to stimulate further work in this
area, we conclude the paper proposing new possible research directions
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